The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

Background 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. Methods To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. Results Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. Conclusions Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting. Supplementary Information The online version contains supplementary material available at 10.1186/s12888-024-05646-x.

Table S1: Note: Composition of basis sets for the study of A) Cheng et al. (1), B) Maddock et al. (2), C) Reid et al.

Participants
Inclusion criteria: native German speaker; right-handed; no diagnosis of schizophrenia, psychosis or autism, or any neurological disease or injury; not currently taking any psychoactive medication within at least the past six weeks and no contraindications for MRI scanning.
Subjects: Out of the participants, three had been previously diagnosed with depression, two had prior eating disorders, one was diagnosed with schizoid personality disorder along with adjustment disorder, and another reported having post-traumatic stress disorder and narcissistic personality disorder.However, only one of the participants was consistently taking antipsychotic medication (fluoxetine, atomoxetine) for attention deficit disorder.
Details of demographic data and symptom scores are shown in Table S2 and have been previously published in (10).See Figure S1 for an overview of our processing, modelling and basis set workflow.

Correlations of metabolite concentration estimates with age
To offer an additional objective marker for the reliability of each method, we explored the relationship between the different metabolite concentrations and age, separated by sex.We, therefore, calculated Spearman's rank correlation coefficients of Glu/tCr, absolute glu and glx with age.The correlation analyses were performed in R using the rstatix package (11) (version 0.7.1).The visualisation of the scatterplots and the statistics in the plots were created with the ggpubr package (12) (version 0.5.0).

Spectral quality
We applied the paired Wilcoxon signed-rank test for multiple pairwise comparisons of the CRLB and the multiple pairwise paired t-tests for the SNR between basis sets.P-values were adjusted using the Bonferroni multiple-testing correction method.An adjusted p-value of less than 0.05 was considered significant.Results are presented in Table S3 -7.

Group differences between the metabolite concentration estimates
Comparisons between the estimates for Glu/tCr, Glu, and Glx are summarized in Figure 4, results are presented in Table S8-S11.Osprey+LCM analyses; Glx, glutamate+glutamine; P-value adj.; adjusted p-value using the Bonferroni multiple-testing correction.

Group differences between the metabolite concentration estimates adding PCr
Comparisons between the estimates for Glx are summarized in Figure S2A.Adding PCr to the basis sets leads to a decrease in the Glx concentrations for both the Rowland(9) and the Kozhuharova(5) basis sets as well as in both toolboxes.Estimates were consistently lower for the analysis in spant+ABfit compared to the ones analyzed with Osprey+LCM.Necessary to note is also that the Rowland+PCr and LCModel basis sets are now identical, which can be nicely seen in the boxplots.

Correlations of each metabolite estimate between the basis sets
As already mentioned in the manuscript, the correlations between metabolite (Glu/tCr, absolute Glu, Glx) and basis set per toolbox revealed that the metabolite concentrations between the basis sets had higher correlations using Ospreys LCM integration than spant+ABfit (see Figure S3).For Osprey+LCM, we found strong correlations between all basis sets (r>0.75).Comparing the Rowland (9) and Kozhuharova (5) basis sets with added PCr to the ones without, the "new" basis sets show higher correlation coefficients between 0.93-1 for Osprey+LCM and 0.88-0.94for spant+ABfit (Figure S3).

Fitting parameters influence the personality traits -glutamate correlations
Finally, we also analyzed Spearman's rank correlations for clinical scores with the extracted concentration scores for the two basis sets with added PCr.The correlations without additionally added PCr can be found in Figure S4.Overall, the results in Osprey+LCM and ant+ABfit displayed now a more homogenous pattern regarding their correlations.Using the Rowland (9)and Kozhuharova(5) basis set with PCr instead of the ones without this metabolite, the maximum difference between the correlation coefficients of Glx and disorganized traits in Osprey+LCM decreased from 0.18 to 0.04.The same can be seen in spant+ABfit where the maximal difference was changing from 0.42 to 0.16 for this correlation.
However, the difference between the toolboxes was still smaller than the difference across the different basis sets and reached a maximum of 0.11, again for the correlation of Glx and disorganized traits.The correlations are shown in Figure S2 B and C.

Correlations of metabolite concentration estimates with age
We could not find any significant correlations between metabolite concentrations and age.Looking at Spearman's rank correlations for Glu/tCr, Glu and Glx with age separated by sex, we also found no significant results (Figure S5 and S6).
These results do not represent the finding of an age-related decrease in glutamate in adolescence and young adulthood, which has been reported in multiple studies across various regions (14-16).One reason for not detecting this effect might be that the age range in our dataset is too narrow.
Furthermore, we can see visual differences between the sexes in both toolboxes regarding the correlation of the metabolite concentrations with age.Whereas in spant+ABfit the correlations between the metabolite concentrations and age tend to be in the opposite direction for female and male participants, Osprey+LCM showed an almost parallel trend.For Osprey+LCM, the glutamate concentrations seem to be overall higher in males than in females.Changes in the glutamate concentration dependent on the sex have also been observed in previous studies (15,17).13 4 Analysis with Gln

Quality assessment of Gln
Table S12 shows the mean values for the CRLB and the metabolite concentration of Gln for each basis set.Compared to the CRLBs of Glu and Glx, the mean values for Gln are around 15% higher and lie between 15.92% for the Reid basis set and 20.51% for the Rowland basis set.As the recommended CRLB exclusion cut-off lies at 20%, 31 participants in our study need to be excluded.As an information for future studies, the analysis of Gln with the reduced number of participants are presented here in the supplements.Comparisons between the estimates for Gln for each analysis method are summarized in Figure S7A.
Similar to the other metabolites estimates (Glu/tCr, Glu, Glx) we found a higher heterogeneity of Gln in spant+ABfit compared to Osprey+LCM based on the visualization of the individual data points and their connection over the different basis sets.

Fitting parameters influence the personality trait-glutamate correlations for Gln
Finally, we analyzed Spearman's rank correlations for clinical scores with Gln of the different basis sets (Figure S7B).Overall, the results in Osprey+LCM displayed a more homogenous pattern regarding the tendency of their non-significant correlations with a maximum difference of 0.2 between the coefficient scores for the correlation of Gln with disorganized traits.Whereas in spant+ABfit the correlations coefficients showed greater variability ranging from positive to negative values, with a maximal difference of 0.86 for the same correlation, which even has been significant for LCModel (4) (r=0.55,p=0.006),Maddock (2) (r=0.52,p=0.013) and Kozhuharova (5) (r=-0.31,p=0.047).These results are comparable to the effects shown with the other metabolite estimates (Glu/tCr, Glu, Glx), but show an even higher inhomogeneity (see Figure S5 and S6).

Correlations of Gln between the basis sets and toolboxes
Correlations between Gln and basis set per toolbox reveal also similar results compared to the other metabolite concentrations (Glu/tCr, Glu, Glx): between the basis sets Gln correlated higher using Ospreys LCM integration than using spant+ABfit.For Osprey+LCM, we found strong correlations between all basis sets (r>0.75).The quantification results in spant+ABfit showed a much higher variability, especially with the basis set of Kozhuharova et al. (5).Between the toolboxes, the correlations were weak to high, whereby the correlations have been generally higher for Gln than for the other basis sets, excluding Kozhuharova et al. (5).Correlation strength was classified according to Akoglu (13).

Figure S2 :
Figure S2: Basisets + PCr: Group comparisons for Glx in the ACC and Correlations between the metabolite

Figure S4 :
Figure S4: Correlations between the metabolite concentrations and clinical scores

Figure S5 :
Figure S5: Correlation of Glu/tCr, Glu and Glx with age by sex for spant+ABfit

Figure S6 :
Figure S6: Correlation of Glu/tCr, Glu and Glx with age by sex for Osprey+LCM

Table S2 :
Demographic data and clinical scores.
Note: Values are mean (SD) SPQ, Schizotypal Personality Questionnaire; AQ, Autism Spectrum Quotient a Wilcoxon rank sum test 2.2 1 H-MRS Processing

Table S3 :
Summary spectral quality parameter distributions of the basis sets Wilcoxon signed-rank test pairwise comparisons for the Glx CRLB of spant+ABfit and Osprey+LCM analyses; CRLB, Cramer-Rao lower bounds; P-value adj., adjusted p-value using the Bonferroni multiple-testing correction.
Note: Multiple pairwise paired t-tests for the SNR of spant+ABfit and Osprey+LCM analyses; SNR, Signal-to-Noise Ratio; P-value adj.; adjusted p-value using the Bonferroni multiple-testing correction.Note: Wilcoxon signed-rank test pairwise comparisons for the glutamate CRLB of spant+ABfit and Osprey+LCM analyses; CRLB, Cramer-Rao lower bounds; P-value adj., adjusted p-value using the Bonferroni multiple-testing correction.

Table S12 :
Quality and Gln concentration